@h100envy: Liquid AI's head of post-training explained how they built a small model that runs on-device under 1 GB in 20 minutes -…
Summary
Liquid AI's head of post-training explains how to build a sub-1GB on-device model in 20 minutes using LFM2.5, on-policy preference alignment, agentic RL, curriculum training, and iterative model merging, achieving tool-calling reliability that beats much larger models.
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Cached at: 08/03/26, 01:45 AM
Liquid AI’s head of post-training explained how they built a small model that runs on-device under 1 GB in 20 minutes - better than $2500 small-model bootcamps.
pick LFM2.5 base -> on-policy preference alignment -> agentic reinforcement learning -> curriculum training -> iterative model merging -> ship a 1B model that reliably calls tools on your phone.
That loop is why frontier small models now beat 70B models on the tasks that actually matter.
LFM2.5 + on-policy DPO + agentic RL + curriculum training + iterative merging - that’s the stack.
Watch and save it, then run a 1B agent on your phone tonight.
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